def run_experiment(name, cname, rname, models, data):
data_by_image = defaultdict(list)
for datum in data:
data_by_image[datum.image_id].append(datum)
with open("experiments/%s/%s.ids.txt" % (name, cname)) as id_f, \
open("experiments/%s/%s.results.%s.txt" % (name, cname, rname), "w") as results_f:
print >>results_f, "id,target,distractor,similarity,model_name,speaker_score,listener_score,description"
counter = 0
for line in id_f:
img1, img2, similarity = line.strip().split(",")
assert img1 in data_by_image and img2 in data_by_image
d1 = data_by_image[img1][0]
d2 = data_by_image[img2][0]
for model_name, model in models.items():
for i_sample in range(10):
speaker_scores, listener_scores, samples = \
model.sample([d1], [[d2]], dropout=False, viterbi=False)
parts = [
counter,
img1,
img2,
similarity,
model_name,
speaker_scores[0],
listener_scores[0],
" ".join([WORD_INDEX.get(i) for i in samples[0][1:-1]])
]
print >>results_f, ",".join([str(s) for s in parts])
counter += 1
def demo(scenes, model, apollo_net, config):
data = scenes[:config.batch_size]
alt_indices = \
[np.random.choice(len(scenes), size=config.batch_size)
for i_alt in range(config.alternatives)]
alt_data = [[scenes[i] for i in alt] for alt in alt_indices]
_, samples = model.sample(data, alt_data, dropout=False)
for i in range(10):
sample = samples[i]
print data[i].image_id
print " ".join([WORD_INDEX.get(i) for i in sample])
print
def sample(self, prefix, encoding, viterbi):
net = self.apollo_net
batch_size = net.blobs[encoding].shape[0]
max_words = 20
l_seed = "LstmStringDecoder_%s_%s_seed"
l_prev_word = "LstmStringDecoder_%s_%s_word_%d"
l_word_vec = "LstmStringDecoder_%s_%s_word_vec_%d"
l_concat = "LstmStringDecoder_%s_%s_concat_%d"
l_lstm = "LstmStringDecoder_%s_%s_lstm_%d"
l_hidden = "LstmStringDecoder_%s_%s_hidden_%d"
l_mem = "LstmStringDecoder_%s_%s_mem_%d"
l_output = "LstmStringDecoder_%s_%s_output_%d"
l_softmax = "LstmStringDecoder_%s_%s_softmax_%d"
p_word_vec = ["LstmStringDecoder_%s_word_vec" % self.name]
p_lstm = [
"LstmStringDecoder_%s_lstm_iv" % self.name,
"LstmStringDecoder_%s_lstm_ig" % self.name,
"LstmStringDecoder_%s_lstm_fg" % self.name,
"LstmStringDecoder_%s_lstm_og" % self.name
]
p_output = [
"LstmStringDecoder_%s_output_weight" % self.name,
"LstmStringDecoder_%s_output_bias" % self.name
]
samples = np.zeros((batch_size, max_words), dtype=int)
samples[:, 0] = WORD_INDEX["<s>"]
net.f(
NumpyData(l_seed, np.zeros((batch_size, self.config.hidden_size))))
for i_step in range(1, max_words):
l_prev_word_i = l_prev_word % (self.name, prefix, i_step)
l_word_vec_i = l_word_vec % (self.name, prefix, i_step)
l_concat_i = l_concat % (self.name, prefix, i_step)
l_lstm_i = l_lstm % (self.name, prefix, i_step)
l_hidden_i = l_hidden % (self.name, prefix, i_step)
l_mem_i = l_mem % (self.name, prefix, i_step)
l_output_i = l_output % (self.name, prefix, i_step)
l_softmax_i = l_softmax % (self.name, prefix, i_step)
if i_step == 1:
prev_hidden = l_seed
prev_mem = l_seed
else:
prev_hidden = l_hidden % (self.name, prefix, i_step - 1)
prev_mem = l_mem % (self.name, prefix, i_step - 1)
net.f(NumpyData(l_prev_word_i, samples[:, i_step - 1]))
net.f(
Wordvec(l_word_vec_i,
self.config.word_embedding_size,
len(WORD_INDEX),
bottoms=[l_prev_word_i],
param_names=p_word_vec))
net.f(
Concat(l_concat_i,
bottoms=[prev_hidden, l_word_vec_i, encoding]))
net.f(
LstmUnit(l_lstm_i,
bottoms=[l_concat_i, prev_mem],
param_names=p_lstm,
tops=[l_hidden_i, l_mem_i],
num_cells=self.config.hidden_size))
net.f(
InnerProduct(l_output_i,
len(WORD_INDEX),
bottoms=[l_hidden_i],
param_names=p_output))
net.f(Softmax(l_softmax_i, bottoms=[l_output_i]))
choices = []
for i in range(batch_size):
probs = net.blobs[l_softmax_i].data[i, :].astype(np.float64)
probs /= probs.sum()
if viterbi:
choices.append(np.argmax(probs))
else:
choices.append(np.random.choice(len(WORD_INDEX), p=probs))
samples[:, i_step] = choices
out_samples = []
for i in range(samples.shape[0]):
this_sample = []
for j in range(samples.shape[1]):
word = WORD_INDEX.get(samples[i, j])
this_sample.append(samples[i, j])
if word == "</s>":
break
out_samples.append(this_sample)
return out_samples
def sample(self, prefix, encoding, viterbi):
net = self.apollo_net
max_words = 20
batch_size = net.blobs[encoding].shape[0]
out_logprobs = np.zeros((batch_size, ))
samples = np.zeros((batch_size, max_words))
history_features = np.zeros((batch_size, len(WORD_INDEX)))
last_features = np.zeros((batch_size, len(WORD_INDEX)))
samples[:, 0] = WORD_INDEX["<s>"]
last_features[:, WORD_INDEX["<s>"]] += 1
l_history_data = "MlpStringDecoder_%s_%s_history_data_%d"
l_last_data = "MlpStringDecoder_%s_%s_last_data_%d"
l_cat_features = "MlpStringDecoder_%s_%s_cat_features_%d"
l_ip1 = "MlpStringDecoder_%s_%s_ip1_%d"
l_cat = "MlpStringDecoder_%s_%s_cat_%d"
l_relu1 = "MlpStringDecoder_%s_%s_relu1_%d"
l_ip2 = "MlpStringDecoder_%s_%s_ip2_%d"
l_softmax = "MlpStringDecoder_%s_%s_softmax_%d"
p_ip1 = [
"MlpStringDecoder_%s_ip1_weight" % self.name,
"MlpStringDecoder_%s_ip1_bias" % self.name
]
p_ip2 = [
"MlpStringDecoder_%s_ip2_weight" % self.name,
"MlpStringDecoder_%s_ip2_bias" % self.name
]
for i_step in range(1, max_words):
l_history_data_i = l_history_data % (self.name, prefix, i_step)
l_last_data_i = l_last_data % (self.name, prefix, i_step)
l_cat_features_i = l_cat_features % (self.name, prefix, i_step)
l_ip1_i = l_ip1 % (self.name, prefix, i_step)
l_cat_i = l_cat % (self.name, prefix, i_step)
l_relu1_i = l_relu1 % (self.name, prefix, i_step)
l_ip2_i = l_ip2 % (self.name, prefix, i_step)
l_softmax_i = l_softmax % (self.name, prefix, i_step)
net.f(DummyData(l_history_data_i, (1, 1, 1, 1)))
net.blobs[l_history_data_i].reshape(history_features.shape)
net.f(DummyData(l_last_data_i, (1, 1, 1, 1)))
net.blobs[l_last_data_i].reshape(last_features.shape)
net.blobs[l_history_data_i].data[...] = history_features
net.blobs[l_last_data_i].data[...] = last_features
net.f(
Concat(l_cat_features_i,
bottoms=[l_history_data_i, l_last_data_i]))
net.f(
InnerProduct(l_ip1_i,
self.config.hidden_size,
bottoms=[l_cat_features_i],
param_names=p_ip1))
net.f(Concat(l_cat_i, bottoms=[l_ip1_i, encoding]))
net.f(ReLU(l_relu1_i, bottoms=[l_cat_i]))
net.f(
InnerProduct(l_ip2_i,
len(WORD_INDEX),
bottoms=[l_relu1_i],
param_names=p_ip2))
net.f(Softmax(l_softmax_i, bottoms=[l_ip2_i]))
probs = net.blobs[l_softmax_i].data
history_features += last_features
last_features[...] = 0
for i_datum in range(batch_size):
d_probs = probs[i_datum, :].astype(float)
d_probs /= d_probs.sum()
if viterbi:
choice = d_probs.argmax()
else:
choice = np.random.multinomial(1, d_probs).argmax()
samples[i_datum, i_step] = choice
last_features[i_datum, choice] += 1
out_logprobs[i_datum] += np.log(d_probs[choice])
out_samples = []
for i in range(samples.shape[0]):
this_sample = []
for j in range(samples.shape[1]):
word = WORD_INDEX.get(samples[i, j])
#this_sample.append(word)
this_sample.append(samples[i, j])
if word == "</s>":
break
if this_sample[-1] != WORD_INDEX["</s>"]:
this_sample.append(WORD_INDEX["</s>"])
out_samples.append(this_sample)
return out_logprobs, out_samples
def sample(self, prefix, encoding, viterbi):
net = self.apollo_net
batch_size = net.blobs[encoding].shape[0]
max_words = 20
l_seed = "LstmStringDecoder_%s_%s_seed"
l_prev_word = "LstmStringDecoder_%s_%s_word_%d"
l_word_vec = "LstmStringDecoder_%s_%s_word_vec_%d"
l_concat = "LstmStringDecoder_%s_%s_concat_%d"
l_lstm = "LstmStringDecoder_%s_%s_lstm_%d"
l_hidden = "LstmStringDecoder_%s_%s_hidden_%d"
l_mem = "LstmStringDecoder_%s_%s_mem_%d"
l_output = "LstmStringDecoder_%s_%s_output_%d"
l_softmax = "LstmStringDecoder_%s_%s_softmax_%d"
p_word_vec = ["LstmStringDecoder_%s_word_vec" % self.name]
p_lstm = ["LstmStringDecoder_%s_lstm_iv" % self.name,
"LstmStringDecoder_%s_lstm_ig" % self.name,
"LstmStringDecoder_%s_lstm_fg" % self.name,
"LstmStringDecoder_%s_lstm_og" % self.name]
p_output = ["LstmStringDecoder_%s_output_weight" % self.name,
"LstmStringDecoder_%s_output_bias" % self.name]
samples = np.zeros((batch_size, max_words), dtype=int)
samples[:,0] = WORD_INDEX["<s>"]
net.f(NumpyData(
l_seed, np.zeros((batch_size, self.config.hidden_size))))
for i_step in range(1, max_words):
l_prev_word_i = l_prev_word % (self.name, prefix, i_step)
l_word_vec_i = l_word_vec % (self.name, prefix, i_step)
l_concat_i = l_concat % (self.name, prefix, i_step)
l_lstm_i = l_lstm % (self.name, prefix, i_step)
l_hidden_i = l_hidden % (self.name, prefix, i_step)
l_mem_i = l_mem % (self.name, prefix, i_step)
l_output_i = l_output % (self.name, prefix, i_step)
l_softmax_i = l_softmax % (self.name, prefix, i_step)
if i_step == 1:
prev_hidden = l_seed
prev_mem = l_seed
else:
prev_hidden = l_hidden % (self.name, prefix, i_step - 1)
prev_mem = l_mem % (self.name, prefix, i_step - 1)
net.f(NumpyData(l_prev_word_i, samples[:, i_step-1]))
net.f(Wordvec(
l_word_vec_i, self.config.word_embedding_size,
len(WORD_INDEX), bottoms=[l_prev_word_i],
param_names=p_word_vec))
net.f(Concat(
l_concat_i, bottoms=[prev_hidden, l_word_vec_i, encoding]))
net.f(LstmUnit(
l_lstm_i, bottoms=[l_concat_i, prev_mem],
param_names=p_lstm, tops=[l_hidden_i, l_mem_i],
num_cells=self.config.hidden_size))
net.f(InnerProduct(
l_output_i, len(WORD_INDEX), bottoms=[l_hidden_i],
param_names=p_output))
net.f(Softmax(l_softmax_i, bottoms=[l_output_i]))
choices = []
for i in range(batch_size):
probs = net.blobs[l_softmax_i].data[i,:].astype(np.float64)
probs /= probs.sum()
if viterbi:
choices.append(np.argmax(probs))
else:
choices.append(np.random.choice(len(WORD_INDEX), p=probs))
samples[:, i_step] = choices
out_samples = []
for i in range(samples.shape[0]):
this_sample = []
for j in range(samples.shape[1]):
word = WORD_INDEX.get(samples[i,j])
this_sample.append(samples[i,j])
if word == "</s>":
break
out_samples.append(this_sample)
return out_samples
def sample(self, prefix, encoding, viterbi):
net = self.apollo_net
max_words = 20
batch_size = net.blobs[encoding].shape[0]
out_logprobs = np.zeros((batch_size,))
samples = np.zeros((batch_size, max_words))
history_features = np.zeros((batch_size, len(WORD_INDEX)))
last_features = np.zeros((batch_size, len(WORD_INDEX)))
samples[:,0] = WORD_INDEX["<s>"]
last_features[:,WORD_INDEX["<s>"]] += 1
l_history_data = "MlpStringDecoder_%s_%s_history_data_%d"
l_last_data = "MlpStringDecoder_%s_%s_last_data_%d"
l_cat_features = "MlpStringDecoder_%s_%s_cat_features_%d"
l_ip1 = "MlpStringDecoder_%s_%s_ip1_%d"
l_cat = "MlpStringDecoder_%s_%s_cat_%d"
l_relu1 = "MlpStringDecoder_%s_%s_relu1_%d"
l_ip2 = "MlpStringDecoder_%s_%s_ip2_%d"
l_softmax = "MlpStringDecoder_%s_%s_softmax_%d"
p_ip1 = ["MlpStringDecoder_%s_ip1_weight" % self.name,
"MlpStringDecoder_%s_ip1_bias" % self.name]
p_ip2 = ["MlpStringDecoder_%s_ip2_weight" % self.name,
"MlpStringDecoder_%s_ip2_bias" % self.name]
for i_step in range(1, max_words):
l_history_data_i = l_history_data % (self.name, prefix, i_step)
l_last_data_i = l_last_data % (self.name, prefix, i_step)
l_cat_features_i = l_cat_features % (self.name, prefix, i_step)
l_ip1_i = l_ip1 % (self.name, prefix, i_step)
l_cat_i = l_cat % (self.name, prefix, i_step)
l_relu1_i = l_relu1 % (self.name, prefix, i_step)
l_ip2_i = l_ip2 % (self.name, prefix, i_step)
l_softmax_i = l_softmax % (self.name, prefix, i_step)
net.f(DummyData(l_history_data_i, (1,1,1,1)))
net.blobs[l_history_data_i].reshape(history_features.shape)
net.f(DummyData(l_last_data_i, (1,1,1,1)))
net.blobs[l_last_data_i].reshape(last_features.shape)
net.blobs[l_history_data_i].data[...] = history_features
net.blobs[l_last_data_i].data[...] = last_features
net.f(Concat(l_cat_features_i, bottoms=[l_history_data_i, l_last_data_i]))
net.f(InnerProduct(
l_ip1_i, self.config.hidden_size, bottoms=[l_cat_features_i],
param_names=p_ip1))
net.f(Concat(l_cat_i, bottoms=[l_ip1_i, encoding]))
net.f(ReLU(l_relu1_i, bottoms=[l_cat_i]))
net.f(InnerProduct(
l_ip2_i, len(WORD_INDEX), bottoms=[l_relu1_i],
param_names=p_ip2))
net.f(Softmax(l_softmax_i, bottoms=[l_ip2_i]))
probs = net.blobs[l_softmax_i].data
history_features += last_features
last_features[...] = 0
for i_datum in range(batch_size):
d_probs = probs[i_datum,:].astype(float)
d_probs /= d_probs.sum()
if viterbi:
choice = d_probs.argmax()
else:
choice = np.random.multinomial(1, d_probs).argmax()
samples[i_datum, i_step] = choice
last_features[i_datum, choice] += 1
out_logprobs[i_datum] += np.log(d_probs[choice])
out_samples = []
for i in range(samples.shape[0]):
this_sample = []
for j in range(samples.shape[1]):
word = WORD_INDEX.get(samples[i,j])
#this_sample.append(word)
this_sample.append(samples[i,j])
if word == "</s>":
break
if this_sample[-1] != WORD_INDEX["</s>"]:
this_sample.append(WORD_INDEX["</s>"])
out_samples.append(this_sample)
return out_logprobs, out_samples